Extraction of cardiac signal data

ABSTRACT

A T-wave offset point of an ECG signal is provided. In accordance with various example embodiments, a location of a QRS complex in the ECG signal is identified and used to determine a first time window of the ECG signal in which to search for a T-wave offset point. The T-wave offset point is identified within the first time window, and the identified T-wave offset point is provided as an output based upon a noise characteristic of the ECG signal in a second time window that includes at least a portion of the T-wave.

RELATED PATENT DOCUMENTS

This patent document is a continuation under 35 U.S.C. §120 of U.S.patent application Ser. No. 13/172,415 filed on Jun. 29, 2011 (U.S. Pat.No. 8,433,395), which claims the benefit under 35 U.S.C. §119 of U.S.Provisional Patent Application Ser. No. 61/359,462, filed on Jun. 29,2010, and 61/370,026, filed on Aug. 2, 2010; U.S. patent applicationSer. No. 13/172,415 is also a continuation-in-part of U.S. patentapplication Ser. No. 12/938,995, filed on Nov. 3, 2010 (U.S. Pat. No.8,632,465) and which claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/257,718, filed on Nov. 3, 2009, and of U.S.Provisional Patent Application Ser. No. 61/366,052, filed on Jul. 20,2010, to all of which priority is claimed via 35 U.S.C. §120 for commonsubject matter; each of these patent documents is fully incorporatedherein by reference.

FIELD OF INVENTION

The present invention relates to measurement of cardiac interval andextraction of other cardiac repolarization information from an ECG ofhuman or animal subjects.

BACKGROUND

The cardiac repolarization period of the cardiac cycle, primarilyconsisting of the T-wave, is of interest for a variety of uses,including the analysis of cardiac function. For instance, repolarizationabnormalities can be associated with dangerous arrhythmias, which aredesirably detected for use in assessing cardiac function, ongoing healthmonitoring and/or treating cardiac pathologies. The QT interval (thetime between the start of a Q-wave and the end of a T-wave) isfrequently measured as an indicator of repolarization time withlonger-than-normal or shorter-than-normal QT interval associated withpossible risk of life-threatening arrhythmias. Evaluation of QT intervalas an indicator of risk of life-threatening arrhythmias can involvemeasurement of average QT interval, QT interval dynamics, or both.Regulatory agencies can require that QT interval be measured in bothanimal models and human subjects during the course of developing newdrugs as a means of assessing potential for drug-induced arrhythmias. QTinterval measurements are also used to guide therapies in clinical care.Beyond measurement of QT interval, cardiac repolarization can beevaluated for clinical care and research using other methodologiesincluding T-wave alternans, T-wave complexity, T-wave variability, andT-wave morphology changes.

Accurate measurement of QT interval has been challenging as a result ofdifficulties in accurately and consistently identifying T-wave offsetdue to its flat pattern, especially in the presence of noise. Theaccuracy of results produced by current methods is compromised, however,by noise in the ECG and by difficulty in accurately identifying T-waveoffset. Further, approaches to identifying T-wave offset have sufferedfrom an inability to accurately determine whether a particular T-waveoffset is accurate, or whether the result may have been compromised dueto the presence of noise, certain arrhythmias or difficultrepolarization wave morphology. These and related matters have presentedchallenges to the measurement of QT interval, assessment of QT intervaldynamics, and isolation of the cardiac repolarization signal of an ECG.

SUMMARY

Various aspects of the present invention are directed to devices,methods and systems involving evaluating repolarization activity of theheart as represented in the ECG of a human or animal subject, in amanner that addresses challenges including those discussed above.

In connection with various example embodiments, T-wave offset points forECG signals are provided as an output, based upon a noise characteristicof an ECG signal in a second time window that includes at least aportion of the T-wave. In certain implementations, the location of a QRScomplex in the ECG signal is identified and used for determining a firsttime window of the ECG signal in which to search for the T-wave offsetpoint, and the T-wave offset point is identified within the first timewindow.

According to another example embodiment, a T-wave offset point isidentified in an ECG. The ECG is decomposed into subcomponents in asecond domain in which at least a portion of the subcomponentsrepresenting noise are independent of a portion of the subcomponentsrepresenting the signal. The noise and signal subcomponents areseparated, which separation is used as a basis upon which the T-wave isprovided.

The separation of the signal and noise subcomponents can be accomplishedin a variety of manners. In some embodiments, the subcomponents areseparated using one or more of spatially selective filtering, principalcomponent analysis, independent component analysis and periodiccomponent analysis. One or more subcomponents associated with the T-waveof the ECG are separated from other signal subcomponents within thesecond domain and used to evaluate a noise characteristic in thevicinity of the T-wave offset point.

According to another example embodiment, a noise characteristic iscomputed for a portion of the T-wave where the presence of noise canimpact the accuracy of T-wave offset identification. The noisecharacteristic is computed by separating the T-wave energy and the noiseenergy in the portion of the T-wave and using the respective energies tocompute a signal-to-noise ratio or other measures indicative of therelative levels of signal and noise energy in the portion. T-wave energyand noise energy in the portion can be separated by a number oftechniques including band-pass filtering, wavelet thresholding,multidomain signal processing, or adaptive filtering.

According to another example embodiment, an emphasis signal is computedthat exaggerates inflections in the signal and transition points of theemphasis and denoised signals, such as peaks, valleys, and baselinepoints, are detected to identify the T-wave offset point. Thesetransition points may be detected in one or more of a variety ofmanners, using one or more approaches as described herein, such as viathe analysis of subcomponents separated in accordance with the above.

According to another example embodiment, the subcomponents used tocompute the emphasis signal are denoised using at least one of spatiallyselective filtering, principal component analysis, independent componentanalysis and periodic component analysis to improve consistency andaccuracy of detecting transitions within the emphasis signal. Inadditional embodiments, the denoised subcomponents are used toreconstruct a denoised ECG.

According to another example embodiment, a validity-type metric iscomputed to assess the validity of a T-wave offset point, which can beused to automatically include the T-wave offset point as an outputindicative of an accurate ECG characteristic. The validity metric iscomputed based on noise (e.g., using a dynamic signal-to-noise ratio)computed for a portion of the T-wave of a cardiac cycle as the ratio ofenergy in said signal subcomponents and noise subcomponents. In someembodiments, the validity metric is computed based upon the presence ofventricular or atrial fibrillation, ventricular tachycardia, an RR or QTinterval outlier, QT dispersion and the degree of T-wave complexity.

According to yet another example embodiment, a cardiac repolarizationsignal is constructed by identifying T-wave offset and T-wave onset. Aportion of an ECG between (or about between) T-wave onset and T-waveoffset is isolated from the remainder of the ECG to construct a timeseries consisting primarily of repolarization activity, which can thenbe subsequently analyzed to evaluate T-wave alternans, T-wave morphologychanges, T-wave complexity, T-wave variability, or other methods ofanalysis.

The above summary is not intended to describe each embodiment or everyimplementation of the present disclosure. The figures and detaileddescription that follow more particularly exemplify various embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be more completely understood in consideration of thefollowing detailed description of various embodiments of the inventionin connection with the accompanying drawings, in which:

FIG. 1A is a data flow for an approach to providing a T-wave offset,consistent with one or more example embodiments of the presentinvention;

FIG. 1B provides an example high-level data flow for computing a QTinterval, as may be implemented in connection with FIG. 1A, consistentwith one or more example embodiments of the present invention;

FIG. 2 provides an example detailed data flow and decision diagram forcomputing a QT interval, consistent with one or more example embodimentsof the present invention;

FIGS. 3 and 3A provide an example data flow and decision diagram andrelated plots for creating a cardiac repolarization signal, consistentwith one or more example embodiments of the present invention;

FIGS. 4, 4A, 4B and 4C provide an example data flow and decision diagramand related plots for identifying T-wave offset using an emphasissignal, consistent with one or more example embodiments of the presentinvention;

FIG. 5 provides an example data flow and decision diagram for computinga validity metric, consistent with one or more example embodiments ofthe present invention;

FIGS. 6, 6A, 6B and 6C provide example data flow block diagrams andexample waveforms for a technique that provides a systematic adjustmentof T-wave offset involving clustering of T-waves with similar morphologyfollowed by alignment of T-waves within the cluster, consistent with oneor more example embodiments of the present invention;

FIG. 7 provides an example embodiment of a system that employs a batteryor passively powered subject device for collecting ECG in communicationwith a data collection or review system, consistent with one or moreexample embodiments of the present invention;

FIG. 8 provides an example data flow diagram for computing a dynamicsignal-to-noise ratio, according to another example embodiment of thepresent invention; and

FIG. 9 shows another flow diagram involving the processing of an ECGsignal to provide a T-wave offset, in accordance with another exampleembodiment of the present invention.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe scope of the invention including aspects defined in the claims.

DETAILED DESCRIPTION

Aspects of the present invention relate to methods and devices forassessing repolarization activity. More particular aspects are directedto identifying accurate, consistent, and valid T-wave offset pointswithin an ECG of human or animal subjects. Identification of accurate,consistent, and valid T-wave offset feature points in an ECG is usefulin connection with measurement of QT interval and for creating a cardiacrepolarization signal for evaluation of repolarization activity of aheart.

In connection with various embodiments of the present invention,accurate and consistent QT interval measurements are for a variety ofapplications such as those involving measurement of the QT interval ofambulatory subjects in environments susceptible to noise. The noisecontained in these signals can vary in character and composition. Formany applications, these signals include in-band noise whereby thespectral content of the noise overlaps with the spectral content of theECG signal, which can present challenges to the detection/measurementand use of signals as discussed herein. In-band noise can be problematicbecause, unlike noise with spectral content outside the signalbandwidth, removing in-band noise without introducing distortion in theECG signal can be particularly challenging. Various embodiments of thepresent invention are directed to the processing of signals havingreduced in-band noise, to facilitate a determination of a validity-typeaspect of the signals for evaluation thereof, and related provision ofsignals (e.g., as an output) with a relatively high degree of confidencein the validity of the signals. Certain embodiments are also directed tothe reduction of in-band noise and, in many applications, reducing suchin-band noise without significantly distorting the ECG signal. Theseembodiments may be implemented with the analysis of the portion of theECG associated with cardiac repolarization, thereby improving theaccuracy and consistency of subsequent analysis of repolarizationactivity.

The T-wave offset point can be selected and provided based upon one ormore of a variety of approaches. In connection with one embodiment, aT-wave offset point of an ECG signal is provided as follows. Thelocation of a QRS complex in the ECG signal is identified and used fordetermining a first time window of the ECG signal in which to search forthe T-wave offset point. The T-wave offset point is identified withinthe first time window, and the identified T-wave offset point isprovided as an output based upon a noise characteristic of the ECGsignal in a second time window that includes at least a portion of theT-wave.

In other embodiments, the identified T-wave offset point is providedbased upon a comparison of the noise characteristic to a threshold.Other embodiments are directed to providing the identified T-wave offsetpoint based upon a presence, in the cardiac cycle, of at least one of:atrial fibrillation, QT dispersion in a multi-lead ECG exceeding athreshold, T-wave morphology complexity exceeding a predefinedthreshold, ventricular ectopy, a QT interval measurement that fallsoutside of a user-defined physiologic outlier value or a statisticaloutlier value. In certain embodiments, the identified T-wave offsetpoint (or a plurality of such identified points) is used to assemble atime series of the provided QT interval values, which can be subjectedto analysis of variability.

The second time window can be defined in one or more manners to suitvarious embodiments and applications, and may include the identifiedT-wave offset point. In some embodiments, the duration of the secondtime window can be set to about 30% of the duration of a nominal QTinterval of the ECG signal, and extends beyond the T-wave offset pointby about 10% of the nominal QT interval duration. In one example, thesecond time window may have a duration of about 50 msec and be centeredon about (e.g., within a few milliseconds of) the identified T-waveoffset point. In another embodiment, the second time window extends fromabout the point of a largest deflection of the T-wave from anisoelectric line of the ECG signal to (or near) the T-wave offset point.In other embodiments, the second time window includes a time periodextending from about a QRS offset point in the ECG signal to about theT-wave offset point. In still other embodiments, the second time windowincludes a time period extending the full duration of a cardiac cycle inthe ECG signal.

The noise characteristic may be obtained in one or more of a variety ofmanners, depending upon the application. In some embodiments, the noisecharacteristic is computed as follows. The ECG signal is decomposed intosubcomponents, and subcomponents in the second time window areidentified as primarily associated with either noise or the T-wave ofthe underlying ECG signal. A denoised signal is computed for the secondtime window by combining the subcomponents that are primarily associatedwith the T-wave. A noise signal is computed for the second time windowby combining the residual subcomponents not primarily associated withthe T-wave (e.g., the residual components being those not combined toform the denoised signal). The noise characteristic is then computedbased upon aspects of the noise and/or denoised signal. For example, thenoise characteristic can be computed using one or more of energy of thenoise signal, standard deviation of the noise signal, zero crossingdensity of the noise signal, a metric of noise amplitude computed usingan envelope of the noise signal, and a metric of energy of the denoisedsignal relative to energy of the noise signal. Further, metric of energyof the denoised signal relative to energy of the noise signal may be asignal-to-noise ratio of the ECG signal within the second time window.

In other embodiments, the noise characteristic is computed as follows. Adenoised signal is computed for the second time window using at leastone of a band-pass filter, wavelet thresholding, and an adaptive filterthat passes primarily T-wave energy. A residual of the step of computingthe denoised signal is captured as the noise signal for the second timewindow. The noise characteristic is computed based upon one or both ofthe noise and denoised signals, such as by using one or more of: energyof the noise signal, standard deviation of the noise signal, zerocrossing density of the noise signal, a metric of noise amplitudecomputed using an envelope of the noise signal, and a metric of energyof the denoised signal relative to energy of the noise signal.

With respect to the above and other discussion herein, the termenvelope, as applied to a noise signal by way of example, refers to acurve joining the successive peaks of the noise signal followingsmoothing or low-pass filtering. One approach for computing the envelopeinvolves applying a Hilbert transform of the noise signal, computing theabsolute value of the transform output, and then low-pass filtering theabsolute values.

Another example embodiment is directed to a method for providing arepolarization signal for a cardiac cycle of an ECG signal. The locationof a QRS complex in the cardiac cycle is identified and used to furtheridentify T-wave onset and offset points in the signal, which are in turnused to respectively define the start and end of the repolarizationsignal for the cycle. A noise characteristic of the ECG signal isdetermined for a time window spanning from about the start to about theend of the repolarization signal, and the repolarization signal isprovided as an output, based upon the determined noise characteristic.The noise characteristic can be determined using one or more of avariety of approaches, such as those discussed above.

In some implementations, the repolarization signal is provided as anoutput based upon a comparison of the noise characteristic to athreshold. In other implementations, the repolarization signal isprovided as an output based upon the determined noise characteristic andthe presence in the cardiac cycle of at least one of: atrialfibrillation in the ECG signal, a degree of QT dispersion exceeding athreshold when the ECG signal is a multi-lead signal, T-wave morphologycomplexity of the ECG signal exceeding a threshold, ventricular ectopy,and a QT interval measurement that falls outside of a user-definedphysiologic outlier value or a statistical outlier value. In someimplementations, the provided repolarization signal is appended to amatrix of repolarization signals in which a dimension of the matrixcorresponds to the number of cardiac cycles of the ECG signal.

Another example embodiment is directed to a method for providing a timeseries of beat-to-beat QT interval values from a digitized ECG signal ofan ambulatory subject. The location of a QRS complex and a Q-wave onsetpoint of a cardiac cycle of the ECG signal are identified and used fordetermining a first time window of the cardiac cycle in which to searchfor a T-wave offset point for a T-wave in the cardiac cycle. The T-waveoffset point is identified within the first time window, and a QTinterval value is computed using the identified Q-wave onset point ofthe QRS complex and the identified T-wave offset point. The QT intervalvalue is included in a time series of beat-to-beat QT interval values,based upon a noise characteristic of the ECG signal in a second timewindow that includes at least a portion of the T-wave. The noisecharacteristic can be computed in one or more of a variety of manners,such as described above. In various implementations, the series ofbeat-to-beat QT intervals consists of QT interval values having errordue to noise that is less than 2.5% of the mean QT interval of the ECGsignal, and generated using the aforesaid inclusion approach. Such anerror condition can be achieved, for example, as facilitated by the useof a second time window, which may be limited to a range about theT-wave offset point, and further by the use of denoising approaches asdiscussed and/or referenced herein or in the above-noted patentdocuments to which benefit/priority is claimed.

In various implementations, energy that is not primarily associated withT-wave energy in the first time window of the digitized ECG signal issuppressed, prior to identifying the T-wave offset point. Thissuppression can be achieved using, for example, one or more of MDSPdenoising, wavelet threshold denoising, band-pass filtering, andadaptive filtering.

The QT interval value is included in the time series of beat-to-beat QTinterval values, based upon one or more conditions. In oneimplementation, the QT interval can be included based upon a comparisonof the noise characteristic to a threshold value. In anotherimplementation, the QT interval value is included based upon the noisecharacteristic and the presence, in the cardiac cycle, of at least oneof: atrial fibrillation, ventricular ectopy, QT dispersion in amulti-lead ECG exceeding a threshold, T-wave morphology complexityexceeding a predefined threshold, ventricular ectopy, a QT intervalmeasurement that falls outside of a user-defined physiologic outliervalue or a statistical outlier value.

Various embodiments are also directed to implementing one or moreapproaches as discussed herein, via a computer circuit executinginstructions to carry out the various functions. In some embodiments,instructions are executed to repeat the steps as discussed herein tocompute a plurality of QT interval values. Each QT interval value isincluded in a time series of beat-to-beat QT interval values based upona comparison of a noise characteristic of a corresponding ECG signal inthe second window to a predefined threshold.

For general information regarding exemplary aspects includingperformance aspects, as may be effected and/or realized in connectionwith various embodiments of this invention for providing beat-to-beat QTintervals, reference may be made to M. Brockway and R Hamlin,“Evaluation of an algorithm for highly automated measurements of QTinterval,” Journal of Pharmacological and Toxicological Methods, 2011,Article in press., doi:10.1016/j.vascn.2011.05.004, which is fullyincorporated herein by reference.

Many embodiments described here are directed to signal processingapproaches that may be implemented in accordance with those referred toas Multi-Domain Signal Processing (MDSP), and Multi-Domain Filtering(MDF) that uses MDSP to denoise physiologic signals, as exemplified inU.S. patent application Ser. No. 12/938,995, entitled “PhysiologicalSignal Denoising,” which is referenced above and fully incorporatedherein by reference. Various embodiments may implement noise filteringand signal denoising using one or more approaches as described therein.

In the following discussion, reference is made (in brackets) to numberedreferences listed in order near the end of this document, which arefully incorporated herein by reference. These references may assist inproviding general information regarding a variety of fields that mayrelate to one or more embodiments of the present invention, and furthermay provide specific information regarding the application of one ormore such embodiments. Accordingly, one or more embodiments as describedherein may be implemented in accordance with, or otherwise using,information, approaches, devices and systems as may be described inthese references.

Turning now to the figures, and referring to FIG. 1A, a data flowdiagram exemplifies approaches for providing a T-wave offset, consistentwith one or more example embodiments of the present invention. At block120 the location of a QRS complex in the ECG signal is identified, andthe identified location is used at block 130 for determining a firsttime window of the ECG signal in which to search for the T-wave offsetpoint. At block 140, the T-wave offset point is identified within thefirst time window, and the identified T-wave offset point is provided asan output at block 150, based upon a noise characteristic of the ECGsignal in a second time window that includes at least a portion of theT-wave. Various embodiments and implementations as discussed above (andotherwise herein) may be implemented in connection with the approachshown in FIG. 1, including those directed to obtaining and using a noisecharacteristic.

According to example embodiments involving the computation of a QTinterval, and referring to FIG. 1B, an input ECG signal 101 is denoisedand Q-wave onset and T-wave offset points are identified using thedenoised ECG in process 102. In many embodiments, ECG signal 101 hasbeen filtered to remove noise energy outside the bandwidth of the sensedelectrical activity of the heart and has been digitized using ananalog-to-digital converter. Such filtering can be accomplished using avariety of well known methods and the passband of the filter will varydepending upon the species. As an example, a passband often used forhuman ECGs is 0.05 to 100 Hz. In some embodiments, a dynamicsignal-to-noise ratio (dSNR) and a validity metric (VM) are computed inprocess 102 and used to assess the validity of the onset and offsetpoints as well as ECG sensor integrity. In some embodiments, dSNR and VMare computed for a portion of the T-wave where the presence of noise canimpact the accuracy of T-wave offset detection. In other embodiments, VMcan be computed for a complete cardiac cycle. VM is evaluated relativeto predetermined thresholds, T1, T2 and T3, in decision processes 103,107 and 111 where T3>T2>T1. Evaluation of the VM is useful to determinethe disposition of the information derived from a cardiac cycle and inparticular, can be useful in determining if the information should beretained, discarded, or if there is value in having a human being reviewthe QT interval measurement result provided by the algorithm. In someembodiments, the QT interval is computed if the VM>T2, and is otherwiseclassified as uninterpretable in process 105 (e.g., the T-wave offsetinformation for that cardiac cycle cannot be determined). Cardiac cyclesdetermined as being uninterpretable may generally refer to signals thathave a noise and artifact level that is so high that review by a humanbeing will render no additional useful information. In some embodimentsa count of cardiac cycles is tallied, in process 106, that aredetermined to be uninterpretable (e.g., VM<T2 and/or discarded) as anindicator of signal quality.

If VM for a particular cardiac cycle is greater than T2 but, in decisionprocess 111, is found to be less than T3, then the information for thatcardiac cycle is classified as uncertain in process 113 and may beflagged for later review by a human being. If, in decision process 111,VM>T3 then the information for that cardiac cycle is classified as validin process 112. In some embodiments, QT interval measured for thosecardiac cycles classified as valid is considered to be sufficientlyaccurate and consistent that review by a human being adds no additionalvalue or accuracy improvement, and those QT interval measurements arehence derived in a fully automated manner and without any humanintervention.

In some embodiments, cardiac cycles classified as uncertain arerecommended for review by a human being. The ECG for cardiac cyclesclassified as uncertain may be visually displayed to a human being alongwith the marker location for Q-wave onset and T-wave offset. The humanoperator can then accept the markers as determined by the algorithm,discard the cardiac cycle computing a QT interval, or move a marker ifit is believed to be incorrectly placed by the algorithm.

In some embodiments, a time series of beat-to-beat QT interval values isfurther analyzed using techniques such as QT interval alternans and QTinterval variability as markers of arrhythmogenic risk. In variousembodiments, techniques used to analyze beat-to-beat QT intervalmeasurements include root mean square of successive differences,standard deviation of successive differences, short-term variabilityusing a mean of successive differences, long-term variability computedas the length of the long axis of the ellipsoid of a Poincaire plot, andmultiscale entropy. In another embodiment, valid QT values are averagedover a predetermined time period (e.g., 30 seconds) to compute a mean QTinterval.

In some embodiments a sensor failure alarm is triggered if the ECG isexcessively noisy or if the ECG signal is absent. Either of theseconditions can lead to a very low dSNR. This may indicate that anelectrode has become loose or has fallen off the subject, or it mayindicate that a lead connecting the electrode to a monitoring device mayhave been broken. In these embodiments dSNR is compared to a thresholdT1 in decision process 103 and if dSNR is <T1, a sensor failure alarm istriggered in process 104. In some embodiments, the sensor failure alarmis only triggered after dSNR has remained below threshold T1 for apredetermined period of time in order to reduce the risk of falsealarms.

According to other example embodiments, FIG. 2 shows more detailedapproaches for computing the QT interval. In an example embodiment, aninput ECG signal 201 is present in a first domain and is composed of anunderlying ECG signal and a noise signal. For the purposes of thisexplanation, underlying ECG signal refers to the sensed electricalactivity generated by the heart. Noise signal refers to sensedelectrical activity generated by noise sources independent of the heartsuch as muscle EMG and measurement artifact caused by dynamic changes inelectrical properties of the tissue-electrode interface of the sensor.Signal 201 is decomposed into subcomponents in a second domain of higherdimension than the first domain in process 202. In some embodiments,decomposition is accomplished using a discrete cosine transform [1],Fourier transform [2], filter bank [3], Gabor transform [4] orKarhunen-Loeve transform [5, 6]. In another embodiment, decomposition isaccomplished using a wavelet-related transform and the decompositionlevels correspond to wavelet scales [7]. In another embodiment,decomposition is achieved by representing the observed signals as alinear combination of basis functions. Signal decomposition embodimentsand the use of subcomponents derived from the decomposition fordenoising (i.e. removal of at least some of the in-band noise containedin the signal), extraction of information from the signal, andevaluation of the accuracy of extracted information is referred to asMulti-Domain Signal Processing (MDSP) by way of example, in thediscussion herein. Use of MDSP techniques for removal of in-band noisefrom a signal is referred to as Multi-Domain Filtering (MDF), also byway of example.

Subcomponents are evaluated in process 203 to determine if thosecontained within a defined time window are primarily associated withnoise or primarily associated with the underlying ECG signal. Each ECGsignal wave (e.g., R-wave, Q-wave, T-wave, and P-wave) contributesenergy to one or more subcomponents. The association of a set of one ormore subcomponents with a particular signal wave (e.g., T-wave,QRS-wave, and P-wave) pertains to the representation of energy containedwithin a signal wave within the associated subcomponents [8]. In someembodiments, spatially selective filtering is employed. In thisembodiment the subcomponents associated with the underlying ECG changeduring the time course of the cardiac cycle. Spatially-dependentselection of subcomponents within the cardiac cycle is determinedaccording to time location of the subcomponents relative to the QRScomplex. For example, once a QRS complex is identified, the approximatelocation of the T-wave is known. Subcomponents primarily associated withT-wave energy are selected as related to the underlying ECG signalduring the time spanning the approximate T-wave location, and thosesubcomponents not associated with T-wave energy during this time spanare said to be primarily associated with noise. Subcomponents maycontain both energy of the underlying ECG signal and noise energy. Forthe purpose of this discussion, subcomponents are said to be associatedwith a particular signal wave of the ECG if more than about 50% of theenergy in the subcomponent is energy of the underlying ECG signal wave.

Determining whether a subcomponent is primarily associated with noise orECG signal, or denoising the subcomponents within the second domain, canbe accomplished by using one or more of principal component analysis(PCA), independent component analysis (ICA), periodic component analysis(πCA) and spatially selective filtering (SSF). PCA and ICA areapplicable to multi-lead ECG, while πCA and SSF can be applied to eithermulti-lead or single-lead ECG. The PCA technique [9, 10] usessubcomponent covariance information to orthogonalize subcomponents. Theorthogonalized subcomponents with low signal power are often associatedwith noise and can be removed to achieve denoising. PCA can be used as apreliminary step prior to applying an ICA technique. The ICA techniquefurther separates signal and noise sources [11] as a solution of anoptimization problem that maximizes independence between them. The πCAtechnique computes and jointly diagonalizes covariance andautocorrelation matrices of subcomponents to separate them based ontheir periodicity or quasi-periodicity. [12, 13] The πCA techniqueextracts most periodic subcomponents corresponding to ECG rhythm and,since noise is not generally periodic, it is left behind.

SSF techniques [14, 15, and 16] detect signal-related features and passthem across the subcomponents while blocking features inherent to noise,relying upon the differences of noise and signal distributions acrossdecomposition levels. In one embodiment, an SSF approach is used toexploit the fact that most noise subcomponents are confined todecomposition levels that represent high frequencies. In thisembodiment, the locations of signal features are identified by examiningsubcomponents corresponding to lower frequency. The QRS location isidentified, and a QRS window surrounding the detected QRS wave iscreated. The subcomponents associated with high frequencies arepreserved within the QRS time window. The remainder of the cardiac cycleis assigned to a second time window where the high-frequencysubcomponents are zeroed. QRS wave location for creating the first timewindow can be identified as high amplitude changes in peaks and valleysthat occur simultaneously across multiple decomposition levelsassociated with lower frequencies. Techniques such as correlation ormultiplication of these low-frequency subcomponents can be useful forcomputing an emphasis signal that provides for improved detection of theQRS complex in a noisy ECG. The subsequent emphasis signal can be passedthrough a constant or adaptive threshold detector to locate the QRScomplex.

In another embodiment, some artifacts can be detected as large peakspresent in subcomponents corresponding to high frequency. Theseartifacts can then be removed by zeroing subcomponents where the largepeaks were detected. By zeroing out the subcomponents or time segmentswithin subcomponents associated with noise, and reconstructing the ECGsignal using those subcomponents associated with the ECG signal, thein-band noise level in the ECG is substantially reduced, or “denoised”,to create a denoised ECG. A denoised signal can be reconstructed byperforming the inverse of the transform used to decompose the signal onthe denoised subcomponents. For general information regarding exampledenoising techniques that can be used in this and related embodiments,reference may be made to U.S. patent application Ser. No. 12/938,995,referenced above.

In some embodiments signal energy relative to the noise energy in secondwindow 218 is computed as a signal-to-noise ratio (SNR) as in process204. In one embodiment second window 218 spans from about the T-wavepeak to shortly after the T-wave offset point. In other embodiments, theduration of second window 218 is a percentage of the QT interval, 30%for example. In this embodiment ⅔ of the duration of window 218 spansbefore T-wave offset and ⅓ extends after T-wave offset. In anotherembodiment, window 218 has a fixed duration for a given species. Forexample, for human beings window 218 may be about 50 msec in durationand is approximately centered on the T-wave offset point.

In one embodiment, referring to FIG. 8, SNR (dSNR) is computed as theratio of the energies in signal and noise subcomponents. Input signal801 in a first domain is decomposed into subcomponents, referred to asset D2sub, in a second domain of higher dimension in process 802.

The dimension of the first domain is evaluated in decision process 803.If the dimension of the first domain is larger than 1 then a PCA or ICAtechnique can be used to identify at least some of the subcomponentsprimarily associated with noise in process 804. The noise subcomponentsextracted at this initial denoising step are discarded and the residualnoise and signal subcomponents are evaluated using SSF or πCA in process805. The noise subcomponents identified in process 805, D2n, are used tocompute an estimate of noise energy in process 806. The residualsubcomponents, D2s, are used to compute an estimate of signal energy inprocess 806.

If the dimension of the first domain evaluated in decision process 803equals 1 (e.g., a single channel ECG signal), then subcomponentsprimarily associated with noise, referred to as set D2n, are identifiedusing SSF or πCA in process 807. The residual subcomponents, D2s, arethose primarily associated with an underlying ECG signal. In thesecontexts, signals primarily associated with noise are those signalshaving an energy value of which at least half is from noise components.Similarly, signals primarily associated with an underlying ECG signalare those having an energy value of which at least half is from anunderlying ECG signal. In process 808, an estimate of noise energy iscomputed using subcomponents D2n and an estimate of signal energy iscomputed using subcomponents D2s.

Once noise energy and (underlying ECG) signal energy are computed inprocesses 806 and 808, dSNR is then computed in process 809 according toformula:

${{SNR}_{dB} = {{10{\log_{10}\left( \frac{P_{signal}}{P_{noise}} \right)}}:}},$where P_(signal) and P_(noise) are respective signal and noise energy.Using this approach, dSNR can be updated on a sample-by-sample basis,and can likewise be computed for a group of sample points or a timewindow. In one embodiment dSNR is computed for each cardiac cycle. Inother embodiments, dSNR is computed for a time window surrounding thelocation of a detected feature point (e.g., T-wave offset) and can beused to assess validity of the detected feature point. Alternateembodiments may involve updating dSNR more or less often. For example,it may be useful in some embodiments to compute a value of dSNR for awindow of two to ten cardiac cycles and use this value in calculation ofthe validity metric for all cardiac cycles within the window.

In other embodiments, signal-to-noise ratio is estimated usingconventional approaches following denoising of the signal using anMDF-based embodiment as discussed herein, or using conventionalapproaches in combination with the approach(es) described in FIG. 8. Inone embodiment, the noise is measured between signal waves by computingthe peak amplitude and density of zero crossings. In other embodiments,a signal-to-noise ratio is estimated by computing a spectraldistribution of the denoised ECG signal. In this embodiment, peaks inthe spectral distribution are evaluated to determine the relative powerin the spectrum that occurs within and outside of the normal range ofthe QRS complex, T-wave, and P-wave.

In various example embodiments, and referring to FIG. 2, subcomponentsare used to compute T-wave (process 205), QRS (process 206), and Q-wave(process 207) emphasis signals that exaggerate peaks, valleys and slopesof an ECG wave for identifying feature points. As an example, R-waveidentification is accomplished by generating a QRS emphasis signal thathighlights the significant slopes of the R-wave by combiningsubcomponents associated with the R-wave. Likewise, T-wave offsetidentification is achieved by computing an emphasis signal fromsubcomponents associated with the T-wave.

The specific subcomponents associated with the R-wave, Q-wave, or T-wavedepend upon the decomposition technique used, sampling rate, frequencycontent of the signal wave, and the species from which the ECG wasrecorded. As an example, when decomposition is accomplished using adiscrete wavelet-related transform of a human ECG sampled at 250 Hz, theassociated R-wave subcomponents correspond to wavelet scales 2¹ through2⁴. The associated T-wave subcomponents for this same scenariocorrespond to wavelet scales 2³ to 2⁵. In this example, the set ofsubcomponents associated with R-waves and T-waves overlap and arediscriminated based on temporal occurrence by creating a search windowrelative to the QRS complex in process 210. In one embodiment, allsubcomponents associated with a signal wave are combined to create theemphasis signal. In another embodiment, a subset of these subcomponentsis used to compute the emphasis signal. In one embodiment, evaluation ofthe emphasis signal to detect a feature point is accomplished usingtechniques described by Martinez et al [8] based upon examination of thepattern of significant peaks, valleys, and zero crossings within theemphasis signal. In one embodiment or process 208, the R-wave isdetected by applying an adaptive threshold technique [17] to theemphasis signal. In one embodiment of process 213, T-wave offset isidentified as the first baseline point after the last significant peakor valley of the T-wave emphasis signal. In another embodiment acombination of the emphasis and denoised signals are used to detectfeature points.

To identify Q-wave onset in process 214, a search window is created inprocess 211 to identify a time window relative to the R-wave whereQ-wave onset is expected to occur. The denoised subcomponents associatedwith the QRS-wave are used to compute an emphasis signal in process 207that is subsequently evaluated to identify the Q-wave onset point inprocess 214. In one embodiment, Q-wave onset is identified as the lastbaseline point prior to the first significant peak or valley of theQRS-wave emphasis signal. To improve the accuracy of identifying T-waveoffset and Q-wave onset, search windows are created relative to thelocation of the R-wave in processes 210 and 211 to limit identificationof the offset and onset times to windows in time within the cardiaccycle where it would be reasonable for the respective onset and offsetto occur.

Once Q-onset and T-offset are identified in processes 213 and 214, QTinterval is computed in process 215. In some embodiments, adetermination is made as to whether the QT interval is accurate andvalid. This can be accomplished by computing a validity metric (VM) inprocess 212 and then comparing the validity metric to a threshold T3 indecision process 216. If VM>T3, then the QT interval computed in process215 is considered valid. If not, then the QT measurement for thatcardiac cycle is discarded as in process 217. The validity metriccomputed in process 212 is a function of dSNR computed in process 204and rhythm and morphology characteristics computed in process 209.Rhythm and morphology characteristics may include characteristicsindicative of the presence of atrial fibrillation, QT dispersion whenthe ECG signal is a multilead signal, and complexity of T-wavemorphology. In some applications, such as when computing a cardiacrepolarization signal, the validity metric is modulated based upon thepresence of ventricular ectopy such that a repolarization wave isexcluded for a cardiac cycle containing a ventricular ectopy. In someapplications where QT interval is computed, including QT measurementsfrom cardiac cycles containing ventricular ectopy may improve thepredictive value of QT variability assessments [18]. In this applicationit would therefore not be desirable to modulate VM as a function of thepresence of ventricular ectopy.

In an alternate embodiment, the ECG is denoised using MDF techniques andthe feature points of Q-wave onset and T-wave offset can be evaluatedusing traditional techniques in order to measure QT interval. Thisapproach can be useful in some applications because the accuracy offeature point identification can be improved by using a denoised ECG. Inanother embodiment, it is possible to compute T-wave offset using anMDSP embodiment described above and compute Q-wave onset from a denoisedECG using traditional techniques. As an example, Q-wave onset can beidentified using either threshold, derivative-based, or pattern matchingmethods applied to the denoised ECG signal. In the threshold method, aregion that precedes the first peak or valley associated with R-wavewhere at least a few samples are below a pre-specified threshold isfound. The Q-wave onset is determined as a last point in the identifiedregion. In the derivative-based method an emphasis signal is computed bydifferentiating the denoised ECG. A region in the emphasis signal isidentified preceding the first peak or valley associated with the R-waveemphasis signal where at least a few samples are below a predeterminedthreshold. The Q-wave onset is identified as the last point in theregion.

A pattern or template matching method can also be applied to thedenoised ECG. In this approach, a user selected or signal averagedtemplate of the ECG of a representative cardiac cycle is created andsubsequently cross-correlated with the ECG to be analyzed. In anotherembodiment a template can be automatically generated by signal-averagingcardiac cycles for which VM>T3. The QRS onset is identified in thetemplate either manually by the user, by using threshold techniques orby derivative based method applied to the template. Once Q-wave onset isidentified in the template, the template is cross-correlated withcardiac cycles of the ECG to be analyzed. The template is shifted intime to maximize the cross-correlation function of the full cardiaccycle. In some embodiments, the template can be further shifted toimprove the cross correlation between Q-waves of the cardiac cycle underevaluation and the template. The QRS onset is identified from thematched template.

Various embodiments are directed to creating a cardiac repolarizationsignal. In an example embodiment, and referring to FIGS. 3-3A, a cardiacrepolarization signal is constructed by isolating the portion of the ECGsignal corresponding to the T-wave and suppressing (actively orpassively) all other signal waves (e.g., QRS complex, P-wave) and noise.In some embodiments, all or most of the activity in a plurality ofcardiac cycles is suppressed except for the T-wave, or repolarizationwave, and T-waves from multiple cardiac cycles are aligned sequentiallyto form a time series representing a cardiac repolarization signal. Inanother embodiment, the isolated T-waves are joined together to form atime series representing a cardiac repolarization signal. In anotherembodiment a cardiac repolarization signal consists of organizing theisolated T-waves into a matrix. The length of rows in the matrix isdetermined by the sampling rate and the length of the longest T-wave andthe number of rows is determined by the number of valid T-waves in theECG segment for which repolarization activity is to be analyzed. Thematrix rows containing T-waves can be padded with zeros or theisoelectric ECG value to match the longest T-wave in the segment. TheT-waves in the matrix can be aligned by their peak. In someimplementations, a signal that consists primarily of ventricularrepolarization activity is created using these approaches to facilitateanalysis of the repolarization activity of the heart independent ofatrial activity and ventricular depolarization activity and mitigatepotential issues with the high level of spectral energy in the QRScomplex leaking into the bandwidth of the T-wave and biasing the result.

In connection with other embodiments, a denoised cardiac repolarizationsignal is created to facilitate the use of several analysis methods thatcan identify and evaluate repolarization abnormalities and canpotentially improve the accuracy of results provided by these methods ofanalysis. These analysis methods include T-wave alternans, T-wavevariability, T-wave morphology changes, and T-wave complexity. Invarious embodiments, beat-to-beat variability of T wave parameters ismeasured using one or more of the following metrics [18, 19, and 20]:

Standard Deviation of T wave parameter from N consecutive beats=SD

Root mean square of N successive differences of the T waveparameter=RMSSD

Standard deviation of N successive differences of the T wave parameterSDSD

Beat-to-beat variability of successive differences measured as2*SD²−1/2*SDSD²

Short-term variability of the T wave parameter,

${{STV}_{D} = {\sum\limits_{i = 1}^{N}{{{D_{n + 1} - D_{n}}}/\left\lbrack {N\sqrt{2}} \right\rbrack}}},$where D_(n)—is a T wave parameter from n^(th) beat

Long-term variability of the T wave parameter:

${{LTV}_{D} = {\sum\limits_{i = 1}^{N}{{{D_{n + 1} + D_{n} - {2\; D_{mean}}}}/\left\lbrack {N\sqrt{2}} \right\rbrack}}},$where D_(n)—is a T wave parameter from n^(th) beat

In another embodiment nonlinear measures of complexity of repolarizationdynamics such as multiscale entropy [21] are computed to measurevariability beyond a single beat lag.

In one embodiment a denoised cardiac repolarization signal is created byisolating the T-wave in a series of cardiac cycles that have beendenoised using MDF. T-wave isolation is accomplished by identifying theonset of the T-wave (T-wave onset), the offset of the T-wave (T-waveoffset), and then removing those portions of the ECG that fall outsidethe time between T-wave onset and offset of a cardiac cycle (orotherwise using the portions of an ECG signal lying within a time windowgenerally extending from the T-wave onset to offset). One or more of thesteps employed for isolating the repolarization wave for a cardiac cyclecan be accomplished in a manner consistent with the embodimentsdescribed for measuring QT interval for a cardiac cycle. In oneembodiment, and referring to FIG. 3, input signal 301 is decomposed intosubcomponents and the QRS location is identified in process 302.Subcomponents are evaluated to identify those primarily associated withnoise and those primarily associated with signal waves of the underlyingECG signal in process 303 using methods described earlier relating toprocess 203. Processes 302 and 303 are the same as processes 202 and203, respectively, from FIG. 2. T-wave and QRS emphasis signals arecomputed in processes 305 and 306 in a manner such as that described forprocesses 205 and 206. Likewise, the QRS emphasis signal is evaluated todetect R-waves in process 307 in a manner such as described in process208. Similarly, a T-wave offset search window is created in process 309and a QRS offset search window can be created in process 310 in a mannersuch as in processes 210 and 211, respectively. T-wave offset isidentified in process 312 in the same manner as in process 213. In oneembodiment, the beginning of the repolarization wave is identified asQRS offset. QRS offset is identified in process 313 by evaluating theQRS emphasis signal to find the first baseline point after the lastsignificant slope associated with the QRS complex. In an alternateembodiment, the beginning of the repolarization wave is the T-wave onsetpoint and is identified as the last baseline point prior to a firstsignificant peak or valley of the T-wave emphasis signal computed inprocess 305. In another embodiment, T-wave emphasis and denoised signalscan be used to identify T-wave onset.

In some embodiments, a validity metric (VM) is computed to determine ifa repolarization wave from a cardiac cycle should be included in therepolarization signal. Computation of the validity signal for decidingif a repolarization wave should be included or not is accomplished in amanner consistent with that for computing VM for a QT measurement. Asignal-to-noise ratio (SNR) is computed in process 304 for second timewindow 317. In one embodiment, second time window 317 spans from aboutthe T-wave onset point to about the T-wave offset point. Otherembodiments are directed to a time window including a larger or lesserportion of signal including the T-wave. Rhythm and morphology arecharacterized in process 308 and this information is combined with SNRto compute VM in process 311. In some embodiments the value of VM ismodulated according to whether a cardiac cycle contains a ventricularectopic beat.

The accuracy of results obtained from various analysis methods appliedto a repolarization signal can be impacted by the presence of noise andone or more artifacts. In some embodiments, any T-wave obtained from acardiac cycle is excluded from the cardiac repolarization signal, whenthe level of noise or artifact(s) is high enough to impact the accuracyof information derived from the repolarization wave. In one embodiment,a validity metric is computed and only T-waves from those cardiac cycleswhere the validity metric is >T3 are included in the construction of therepolarization signal. In an alternate embodiment, the repolarizationsignal from a cardiac cycle may be classified as valid, uncertain, oruninterpretable according to the value of VM, in a manner similar tothat used to classify and handle QT measurement for a cardiac cycle.Cardiac cycles classified as uncertain may be reviewed by a human beingto determine if the T-wave for that cardiac cycle should be excludedfrom the repolarization signal, or whether it should be included withT-wave onset and offset as marked, or whether the onset or offset timefor the T-wave should be modified. When creating a repolarization signalfor computing T-wave alternans, it may be useful to exclude twoconsecutive cardiac cycles to preserve the pattern of alternans, even ifonly one of two consecutive cardiac cycles is found to have an invalidT-wave.

As consistent with other embodiments described herein, a denoisedcardiac repolarization signal is used to assess repolarizationabnormality using one or more of T-wave alternans, T-wave variability,T-wave morphology changes, and T-wave complexity. T-wave alternans (TWA)are defined as beat-to-beat fluctuations in amplitude, polarity, orshape of a T-wave. In one embodiment T-wave alternans can be measured asspectral energy at one-half the heart rate. In another embodiment, atime-based metric is calculated by separating the cardiac cycles intoeven and odd beats. A weighted average of odd and even beats can then becalculated and TWA is quantified as the difference between the averagedodd and even beats. In another embodiment, a time-frequency based metricof TWA is calculated by performing a stationary wavelet periodogram ofthe repolarization signal. The TWA are quantified as a ratio of power atadjacent wavelet scales.

In other embodiments, repolarization abnormalities are characterized byevaluating aspects of T-wave variability. In one embodiment, featurepoints of valid denoised T-waves are identified using the emphasissignal and variance of the T-wave or T-wave emphasis signal at thesefeature points is computed. These feature points may include thepositive peak amplitude, negative peak amplitude, amplitudeapproximately midway in time between onset and peak, and amplitudeapproximately midway in time between peak and offset. In anotherembodiment, variability and trends in time of the feature pointsrelative to, for example T-wave onset, is analyzed as a means ofcharacterizing repolarization abnormalities and changes. In anotherembodiment, valid denoised T-waves are segmented into equal intervals(e.g., first one-fourth, second one-fourth, etc.) and area or amplitudevariability of each segment is computed for an ECG segment consisting ofseveral (e.g., 10 to 500) cardiac cycles.

Changes in T-wave morphology can be indicative of reduced repolarizationreserve leading to a proarrhythmic scenario. In one embodiment, T-wavemorphology is quantified using extracted feature points of the emphasissignal, as described above, and changes in T-wave morphology metric aretracked over time (e.g., hours, days, weeks, or months). In oneembodiment, at least one of beat-to-beat variability metrics andmultiscale entropy of T-wave parameters can be computed and trended intime. Unusual patterns in T-wave morphology can be used as a marker ofimpending arrhythmia. In one embodiment, these patterns can be detectedas changes in a T-wave complexity metric, with the metric computed asdescribed later.

Various embodiments are directed to computing and evaluating an emphasissignal for detecting Q-wave onset and T-wave offset points. In anexample embodiment, and referring to FIG. 4, an ECG signal 401 isdecomposed 402 into subcomponents in a second domain of higher dimensionthan the first domain. The subcomponents are denoised 403 by identifyingand excluding those subcomponents that are primarily associated withnoise at a particular point in time. An emphasis signal for a signalwave is computed 404 by combining one or more subcomponents that areprimarily associated with the signal wave and the emphasis signal isevaluated 405 for identifying transitions and features within the ECG.Identifying Q-wave onset and T-wave offset, for example, involvescreating an emphasis signal for each signal wave by combining theassociated subcomponents and then identifying appropriate peaks,valleys, and baseline points of the emphasis signal.

In connection with various embodiments, the specific subcomponentsassociated with each signal wave (e.g., T-wave and Q-wave) are used inaccordance with the species, sampling rate, and decomposition methodused. For example, if decomposition is achieved using a discretewavelet-related transform, the species is a human being, and the ECG issampled at 250 Hz, the T-wave associated subcomponents correspond towavelet scales 2⁴ and 2⁵. The emphasis signal that is computed from oneor two of these scales is proportional to the derivative of the filteredversion of the T-wave. Referring to FIG. 4C, two different T-wavemorphologies and the corresponding T-wave emphasis signals are shownwith T-wave offset marked by a vertical dashed line on both the ECGs andthe emphasis signals. The T-wave emphasis signal is evaluated toidentify T-wave offset based upon an evaluation of the pattern of peaks,valleys, and zero crossings. As can be seen in FIG. 4C, T-wave offset isidentified as the first baseline point following the last significantpeak or valley in the T-wave emphasis signal.

The Q-wave emphasis signal is evaluated using similar techniques. In oneembodiment, decomposition subcomponents associated with the Q-wavecorrespond to wavelet scales 2² through 2⁴ assuming a human ECG sampledat 250 Hz and decomposed using a discrete wavelet-related transform.These subcomponents are combined to create an emphasis signal that isevaluated beginning at the peak of the R-wave, going backward in time.Referring to FIGS. 4A-4B, two different QRS morphologies and thecorresponding Q-wave emphasis signals are shown. As can be seen in theFigures, Q-wave onset is identified (as shown by the vertical dashedline) as the first baseline point prior to the first significant peak orvalley of the emphasis signal prior to the location of the R-wave peak.

In some embodiments, noise is removed from the subcomponents in order tocreate a less noisy emphasis signal. Reduction of noise in the emphasissignal facilitates more accurate and consistent detection of peaks,valleys, and baseline points during the process of identifying T-waveoffset, T-wave onset, Q-wave onset, and the R-wave. One denoisingapproach involves identifying subcomponents that are primarilyassociated with noise so that they can be eliminated duringreconstruction of the signal. This is accomplished by using one or acombination of principal component analysis (PCA), independent componentanalysis (ICA), periodic component analysis (πCA) and spatiallyselective filtering (SSF).

The PCA technique can be applied to multi-lead ECG and uses informationon subcomponent covariance to orthogonalize subcomponents. Theorthogonalized subcomponents with low signal power are often associatedwith noise and can be removed to achieve denoising. PCA can be used as apreliminary step prior to applying an ICA technique to multi-lead ECG.The ICA technique can further be used to separate signal and noisesources as a solution of an optimization problem that maximizesindependence between them. The πCA technique can be applied to bothsingle lead and multi-lead ECG and computes and jointly diagonalizescovariance and autocorrelation matrices of subcomponents to separatethem based on their periodicity or quasi-periodicity [12, 13]. The πCAtechnique extracts most periodic subcomponents corresponding to ECGrhythm and, since noise is not generally periodic, it is left behind.

SSF techniques can be used on either multi-lead or single lead ECG anddetect signal-related features and pass them across the subcomponentswhile blocking features inherent to noise. The technique is based uponthe differences in noise and signal distributions across decompositionlevels. In one embodiment, spatially selective filtering is facilitatedby a decomposition whereby signal energy is concentrated in a smallnumber of large subcomponent coefficients while noise is spread outacross many decomposition levels and is represented by smallcoefficients. In another embodiment, spatially selective filteringexploits the fact that most noise subcomponents are confined to levelsthat represent high frequencies. In this embodiment the locations ofsignal features are identified by examining subcomponents correspondingto lower frequency. The subcomponents associated with high frequency arethen zeroed except those locations where the signal features wereidentified.

Computed QT intervals can be used to calculate metrics associated withreduced repolarization reserve and potential proarrhythmic scenario.Such metrics include:

Mean prolongation or reduction of QT interval

Standard Deviation of QT from N consecutive beats (SD)

Root mean square of N successive differences of QT intervals (RMSSD)

Standard deviation of N successive differences of QT intervals (SDSD)

Beat-to-beat variability of successive differences measured as2*SD²−1/2*SDSD².

Short-term variability of QT interval,

${{STV}_{D} = {\sum\limits_{i = 1}^{N}{{{D_{n + 1} - D_{n}}}/\left\lbrack {N\sqrt{2}} \right\rbrack}}},$where D_(n)—is a QT interval from n-th beat

Long-term variability of QT interval:

${{LTV}_{D} = {\sum\limits_{i = 1}^{N}{{{D_{n + 1} + D_{n} - {2\; D_{mean}}}}/\left\lbrack {N\sqrt{2}} \right\rbrack}}},$where D_(n)—is a QT interval from n-th beat

In another embodiment nonlinear measures of complexity of repolarizationdynamic such as multiscale entropy [21] are computed to measurevariability beyond a single beat lag. In another embodiment, changes inQT/RR hysteresis [22] are trended over time.

In another example embodiment, and referring to FIG. 5, a validitymetric (VM) is computed for assessing the validity of a QT intervalmeasurement or a repolarization wave identified in a cardiac cycle. Inone embodiment, and referring to FIG. 5, VM is computed on a cardiaccycle-by-cycle basis. FIG. 5 shows several inputs that can be useful incomputing VM. These inputs include: a) a signal 502 indicating whetheratrial fibrillation (AF) is present in the cardiac cycle, b) QT interval503 for the cardiac cycle, c) whether the cardiac cycle is an ectopicbeat such as a premature ventricular contraction (PVC), d) degree ofcomplexity of the T-wave morphology, and e) signal-to-noise ratio (SNR)for the second time window. When the ECG being evaluated is a multi-leadECG, it can also be useful to employ a measure of the degree ofdispersion of QT (QTD) measurements between leads [23] when computingvalidity. In one embodiment, each of these inputs can be selectivelyenabled in control input 501. For example, in some embodiments the flagis disabled for ectopic beats such that QT values obtained from suchcardiac cycles are considered valid. This can be useful when QTvariability is computed as an indicator of proarrhythmogencity [18]. Inother embodiments, such as when a repolarization wave is being isolatedto construct a repolarization signal time series, the PVC flag isenabled so that ectopic beats are excluded from the repolarization timeseries.

Inputs 502, 503, 504, and 505 are each processed in a manner that if oneof these input signals meets certain criteria and the flag correspondingto the input is enabled, the multiplier Mx is set to 0 in process 520.The absence of one of the input signals 502 to 505 forces Mx=0, Mx=1 forthe cardiac cycle. Mx is passed from process 520 to process 521 and isused as a multiplicand in the computation of VM. Hence if Mx=0, thenVM=0 for that cardiac cycle. Hence, if Mx=0 a QT interval measurement orrepolarization wave for a cardiac cycle would be deemed invalidregardless of input SNR 507.

In some embodiments, input 502 contains information as to whether thecardiac cycle is part of an AF episode. If the cardiac cycle isdetermined to be part of an AF episode in decision process 508, the flagAF is set to a value True in process 515. Otherwise flag AF=False. Ifthe AF Flag is enabled by input 501 and the AF Flag is True, thenprocess 520 will set Mx=0. Modulating the value of VM according to thepresence or absence of AF can be useful because, during AF, P (or F)waves may occur during the T-wave resulting in blurred T-wave offset oronset and distorted T-wave morphology.

In some embodiments, the value of QT for the cardiac cycle is containedin input 503. In decision process 509, the QT value is compared to apredetermined range corresponding to the outer bounds of thephysiological limits of the subject species. If the QT value is outsidethis range, then the QTo Flag is set to True. If the QT value is withinphysiological range, then it is evaluated in decision process 512 todetermine if it is a statistical outlier (e.g., outside of a statisticalrange). In process 512, the QT value is compared to a range definingnormal and outlier values. In one embodiment, an outlier is defined asmean or median+/−3 standard deviations of QT interval for beats in apredefined time window. In some embodiments a QT interval is evaluatedrelative to a predefined limit to avoid discarding valid QT intervalvalues when variability is low.

In some embodiments, in which the ECG signal is from a multi-lead ECG,QT measurements for the cardiac cycle from each of the various leads areinput in 504. QT dispersion is computed in process 510 (e.g., usingmethods described in reference 31 below). A high value of QTD canindicate that there are errors in computing QT interval in one or moreleads due to noise, artifact, or algorithm error [23] and hence thevalidity of the QT measurement may be questionable. In decision process513, QTD is compared to a threshold Tqtd. If QTD exceeds Tqtd, the flagQTDor is set to True. If QTD is <Tqtd, then the flag QTDor is set toFalse. If the flag QTDor is enabled and is True, then the multiplier Mxis set to 0 in process 520.

In some embodiments, input 505 contains information as to the characterof the cardiac cycle. This information is evaluated in decision process511 and, if the cardiac cycle is identified as containing an ectopicbeat, the flag PVC is set to True in process 519. If the beat isdetermined not to be an ectopic beat in decision process 511, then flagPVC is set to False. If flag PVC is enabled and is True, then Mx is setto 0 in process 520.

In some embodiments the T-wave and emphasis signal for the cardiac cycleare input in process 506. A measure of the complexity of T-wavemorphology (CTWM) is computed in process 514. In one embodiment CTVVM iscomputed as the number of significant peaks and valleys in the T-waveemphasis signal during the time from T-wave onset to T-wave offset. Inanother embodiment relative locations of the emphasis signal peaks andvalleys are included in the computation of the CTWM metric. If theT-wave is highly complex, as is the case for a multiphasic T-wave, thenthe validity metric is reduced to reflect that a U-wave may be presentand thus accuracy of identification of the T-wave offset point may becompromised.

In some embodiments, a metric of signal energy relative to noise energyfor the second time window (218 for T-wave offset VM and 317 forrepolarization VM) is input in 507 for use in computing VM. In someembodiments this metric is SNR and is computed as described previously.In one embodiment, SNR for the second time window is computed inprocesses 218 and 304 as the energy contained in the subcomponentsprimarily associated with the T-wave and noise energy is computed fromthe energy contained in the residual subcomponents. In some embodiments,process 521 computes VM as a function of SNR and CTWM multiplied by Mx,where Mx is computed in process 520 and Mx=0 or 1. In some embodiments,VM is maintained at zero for a predetermined number of subsequentcardiac cycles following an ectopic beat to account for hysteresiseffects in QT interval that can occur following an arrhythmic beat. Insome embodiments VM is evaluated and, if found to be below apredetermined threshold Td, the QT measurement for that cardiac cycle isconsidered invalid and is discarded. Likewise, if VM is >than apredetermined threshold Tg then the QT measurement is considered valid.In some embodiments, if VM has a value between Tg and Td it isconsidered uncertain. QT measurements for cardiac cycles where Tg>VM>Tdmay be reviewed by a trained person to manually assign a T-wave offsetpoint.

In some embodiments QRS duration is measured using the Q-onset and QRSoffset fiducial points detected as described herein. As part ofmeasuring QRS duration, the signal energy relative to noise energy maybe measured in one or more windows within which the presence of noisecan impact the accuracy of Q-onset and QRS offset detection in a mannersimilar to that described for T-wave offset.

Various embodiments are directed to adjusting QT measurements tofacilitate and/or improve the accuracy of resulting data. In accordancewith an example embodiment, and referring to FIGS. 6-6C, measurements ofT-wave offset are adjusted using a technique that employs an evaluationof T-wave morphology in order to further improve the accuracy andconsistency of T-wave offset detection results. Improved consistency inT-wave offset measurement can be useful because it leads to reducedmeasurement variability which can translate into a reduction in samplesize in various research studies, including those that employ QTmeasurements to investigate proarrhythmic potential of drugs underdevelopment [24]. The embodiments described here may be useful forimproving the accuracy and consistency of identifying T-wave offset forcomputing a QT interval parameter or for improving the accuracy andconsistency of identifying T-wave offset and T-wave onset for extractinga repolarization signal. A similar approach may also be useful forimproving the accuracy of other feature points such as Q-wave and P-waveonset.

This approach is based upon the assumption that normal physiologicvariability in T-wave offset relative to cardiac depolarization is smallover a period of a few respiratory cycles, providing that T-wavemorphology is consistent over that time period. However, residual noise,inappropriate high pass filter settings [25] or baseline fluctuationscan bias detection of T-wave offset. Such biases can accumulate,resulting in variability of QT interval measurements that is muchgreater than normal physiologic variability. This embodiment is usefulin reducing the impact of these biases on T-wave offset identification.

In this embodiment, and referring to FIG. 6, input ECG 601 is evaluatedin process 602 to identify T-wave offset as described previously. Theemphasis signal is computed in process 603, also using techniquesdescribed previously, and is evaluated in process 604 to identifyT-waves with similar morphology characteristics. Morphologycharacteristics of each T-wave are evaluated and T-waves with commonmorphology 3 n characteristics are assigned to a cluster in process 605.In process 606, T-waves assigned to a cluster are aligned in time arounda common feature point of the T-wave emphasis signal, referred to as analignment point. In one embodiment, the alignment of T-waves in process606 is performed in a manner that preserves the time difference betweenthe alignment point and the initially derived T-wave offset point.Because QT variability can be caused by changes in the QRS duration(e.g., as often occurs as a result of respiratory modulation), it can beuseful to make any adjustment to T-wave offset relative to a referencepoint within the T-wave. In one embodiment, a composite value for T-waveoffset is computed following alignment of T-waves within a cluster. Thecomposite value can be computed as the median, mean or weighted mean ofthe T-wave offset points of the aligned T-waves in the cluster. Theinitially derived T-wave offset points for T-waves in the cluster arethen adjusted to match the composite value. In another embodiment,T-wave offset points in the cluster are adjusted to match the computedmean value. In another embodiment, computing the composite value for thecluster involves removing statistical outliers (e.g., T-wave offsettimes a predefined multiple of S.D. of mean) prior to computing a mean,following alignment of T-waves in process 606, a composite T-wave offsetvalue is computed in process 607 by combining the offset points of thealigned T-waves. In process 608, T-wave offset values for each cardiaccycle in the cluster are adjusted by matching T-wave offset points tothe composite values computed in 608.

In another embodiment the T-waves in a cluster can be averaged tocompute a template automatically. Template matching techniques, such ascross-correlation, can then be used to correct computed T-wave onset andoffset.

In one embodiment, T-wave morphology is evaluated by analyzing the timeand amplitude of significant peaks, valleys, and zero crossings (e.g.,fiducial points) of the emphasis and the denoised signals. In oneembodiment, T-waves having the same number and similar amplitude offiducial points as well as similar time between the fiducial points areassigned to a common cluster. In another embodiment the number andeither time or the amplitude of the fiducial points are used formorphology classification.

In another embodiment, the time scale of a T-wave is compressed orexpanded in order to allow it to match the criteria of a morphologycluster for reducing the number of clusters. Creating this modifiedT-wave facilitates clustering based upon shape of the T-wave rather thantime scale. The T-wave offset point correction for the modified T-waveis adjusted back in time in proportion to the change in time scale madewhen creating the modified T-wave.

Referring to FIGS. 6-6C, relative to respective embodiments, examplesare presented for noisy human T-waves in 609 and derived emphasissignals in 610 and for canine T-waves and derived emphasis signals 611through 614. The rhombus symbols denote the point of detection of T-waveoffset for each of the T-waves shown. In 609, T-waves of similarmorphology have been identified as belonging to a particularclassification by examining peaks and valleys of the emphasis signal.The emphasis signals shown in 610, computed from denoised subcomponentsas described earlier, provides a useful tool for evaluating theclassification of a T-wave since it contains less noise and provides fora more precise and consistent location of features. The T-waves in acluster can be aligned by the location of the first significant peak orvalley of the emphasis signal. The alignment points computed from theemphasis signals of this cluster are shown as a vertical dashed line in610.

An example, shown in 609 and 610, reveals that one of the T-wave offsetpoints (indicated by the right-bottom rhombus in T-wave cluster in 609)is displaced from the others in the cluster by about two sample pointsdue to residual noise. The identified T-wave offset can be repositionedto match mean or median T-wave offsets in the cluster. The examples of611 (T-waves) and 612 (derived emphasis signals) demonstrate thisembodiment as applied to a canine ECG. In 611 and 612 the T-waves andemphasis signals are shown aligned around the first significant peak orvalley of the emphasis signal (indicated by the vertical dashed line in612). As for 611, the majority of T-wave offset points coincide, but oneT-wave offset (left-most rhombus) differs from the others by about 5sample points due to noise. This T-wave offset can be repositioned tomatch the mean or median of the cluster to ensure consistency of T-waveoffset measurement. Alternately, this initially derived T-wave offsetpoint would be identified as an outlier and removed from computation ofthe composite value.

Some embodiments may use more or less stringent criteria to evaluate themorphology characteristics for assigning a T-wave to a cluster. Anexample of a modified embodiment is provided in 613 and 614 whereby amore relaxed definition of the morphology cluster is used. The criteriaused to determine if a T-wave is assigned to a cluster has been relaxedto allow a +/−1 sample point difference in the distance between thefiducial points of the emphasis signal, whereas the examples of 609 to612 require that fiducial points match exactly. The relaxed inclusioncriteria results in a smaller number of clusters used for T-wave offsetcorrection, with a larger number of T-waves in each cluster. As is seenfrom 613 (T-waves) and 614 (emphasis signals) the T-wave offset pointsare more scattered in time. In one embodiment the initially derivedT-wave offsets are replaced by a mean or median of T-wave offsets in thecluster. In an alternate embodiment, statistical outliers are eliminatedfrom computation of the T-wave offset point.

The embodiments described here for measuring and analyzing QT intervaland other metrics of cardiac repolarization activity may be implementedin a variety of platforms, such as those including a computer, processorand/or related circuitry. In one embodiment, a microprocessor (such asan Intel Pentium or Core microprocessors) or microcontroller (such asthe Texas Instruments MSP 430 microcontroller) is configured (e.g.,programmed) to implement one or more embodiments, such as those shown inand/or described in connection with the figures. In other embodiments amainframe computer or a state machine such as may be implemented onsilicon using a hardware description language such as VHDL is used toimplement one or more embodiments. Still other embodiments are directedto the implementation of different aspects of the embodiments describedherein, such as certain computing steps and/or the computation ofcertain values (e.g., a denoised signal), whereas other aspects areimplemented using different steps using other processors/machines and/orat disparate locations, with resulting data communicated appropriately(e.g., via the Internet). Accordingly, some embodiments are directed tocertain post-processing in which certain computations have been made, asmay be relevant, for example, to the computation of a QT interval andrelated characteristics using a provided denoised signal. Certainembodiments involve combining the measurement of QT interval and othermetrics of repolarization activity with other ECG analysis functionssuch as detection of atrial and ventricular arrhythmias and measurementof characteristics such as QRS duration and PR interval.

Some embodiments of the present invention are implemented in a batteryor passively powered device that is worn by or implanted within a humanor animal subject. Referring to FIG. 7, aspects of the present inventioncan be implemented within subject device 704A and 704B while others canbe implemented in data review system 707. In FIG. 7, it is anticipatedthat multiple subjects are monitored and hence there are multiplesubject devices (704A and 704B) and multiple base stations (705A and705B). In one embodiment, input ECG 701 is amplified and filtered toremove out-of-band noise in 702. The conditioned signal from 702 isdigitized by microcontroller 703. Microcontroller 703 and relatedfunctional elements contain a processor, memory, communications module,and other functions necessary to acquire, process, and control operationof subject devices 704A and 704B. In some embodiments, microcontroller(embedded system) 703 processes the conditioned ECG signal to derive QTinterval measurements and repolarization signals and communicates theinformation derived from the ECG via a communication module. Informationis received by base stations 705A and 705B and is forwarded to datareview system 707 via telecom or data network 706.

The computer instructions required to perform the computing operationsof the present invention are programmed (e.g., optimized) using integeror fixed point arithmetic and lifting or B-spline implementation [26,27] of the signal decomposition transform in order to minimize thenumber of clock cycles or machine states required and hence minimizepower consumption. The resulting code can then be implemented, forexample, in an embedded system 703 capable of operating for an extendedperiod of time on power supplied by a small battery. In such anembodiment, a portion of the computations required to analyzerepolarization activity and extract other information on heart rhythmsmay be implemented within subject device 704A and 704B while others maybe implemented in data review system 707. In another embodiment, thesubject device only records the ECG of the subject and the ECG recordingis processed off line on data review system 707. Data review system 707may include a review function that facilitates human review of ECGs thatwere classified as uncertain by the algorithm.

In another example embodiment, and referring to FIG. 9, a sensed ECGsignal 901 is received and processed to provide a T-wave offset. Thereceived signal is amplified and filtered in 902 to remove signalcontent that is outside the bandwidth of the ECG. The filter employed in902 may include one of a number of filters, such as a multi-poleButterworth filter. For human ECGs, the lower and upper (−3 dB) filtercutoff points can be 0.05 and 100 Hz, respectively. The amplified andfiltered signal is also digitized in 902 for processing by a computingelement such as a microprocessor, signal processing element, or statemachine where steps 903 through 912 are implemented via software,firmware, or hardware description language. In step 903, the QRS complexis identified and the location of the QRS complex is used to define asearch window within which to search for T-wave offset, using one ormore approaches such as those described herein.

The ECG signal is then filtered and/or denoised in step 905 to removethe energy outside the frequency components of the T-wave. Step 905 isaccomplished by one of MDSP denoising, band-pass filtering, waveletthresholding [30], and adaptive filtering. The output denoised T-wave isprocessed to identify T-wave offset in step 907, and a noise signal iscomputed in step 906 as the residual of the T-wave denoising process. Inone embodiment, the noise signal can be computed in 906 by subtractingthe denoised T-wave signal computed in step 905 from the digitized ECGsignal produced in step 902.

The T-wave offset point identified in step 907 is used in step 908 toidentify a second time window. This second time window includes aportion of the T-wave where the presence of noise can impact theaccuracy of T-wave offset detection and the considerations for durationand location of this time window are similar to those used to definesecond time windows 218 and 317. In some embodiments this time windowincludes the T-wave offset point and may also include a range of timepreceding the T-wave offset point and a range of time following theT-wave offset point. In some embodiments the second time window beginsat the first significant peak of the T-wave, where the first significantpeak is defined as either the highest amplitude positive or negativepeak or the first peak preceding the highest amplitude peak that isgreater than 50% of the amplitude of the highest peak. In someembodiments the second time window begins at the highest positive ornegative T-wave peak. In other embodiments the duration of the secondtime window is set to about ⅓ of the duration of the nominal QT intervalfor the species for which QT interval is being measured. In thisembodiment the second window may be positioned so that it terminates ata point corresponding to a distance of about 10% of the nominal QTinterval after the T-wave offset point, with about 90% of the durationof the second window preceding the T-wave offset point. In anotherembodiment, the second window has a fixed duration, such as about 50msec, and is roughly centered on the T-wave offset point.

In step 909 a noise characteristic is computed for the second timewindow for use in determining whether the T-wave offset point isaccurate and/or valid. In one embodiment, the noise characteristic is asignal-to-noise ratio computed using the denoised T-wave and noisesignals present in the second time window. In another embodiment, thenoise signal present in the second time window is used to compute noiseenergy present in the second time window. In another embodiment, thenoise signal present in the second time window is used to compute astandard deviation of the noise in the second time window. In anotherembodiment, the noise signal in the second time window is used tocompute a zero crossing density in the second time window. In yetanother embodiment, a metric of noise amplitude is computed using anenvelope. This can be accomplished by applying a Hilbert transform tothe noise signal in the second time window and computing an absolutevalue of the transform output. The absolute value output is thenlow-pass filtered to compute an envelope of the noise signal.

In step 910, the noise characteristic computed in step 909 is comparedto a threshold. In an embodiment in which the noise characteristic iscomputed as SNR, SNR is compared to a threshold and, if >the threshold,the identified T-wave offset point is considered to be accurate andvalid in 912. If it is less than the threshold, it is identified aspossibly inaccurate in 911 and may not be included in subsequentanalysis. In one embodiment, the threshold evaluated in 910 is avalidity metric (VM). In one embodiment, VM is computed as describedpreviously and, referring to FIG. 5, SNR computed in 909 corresponds toinput 507.

For general information regarding a variety of fields that may relate toone or more embodiments of the present invention, and for specificinformation regarding the application of one or more such embodiments,reference may be made to the following documents, which are fullyincorporated herein by reference. Various ones of these references arefurther cited above via corresponding numerals, and may be implementedas such.

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Based upon the above discussion and illustrations, those skilled in theart will readily recognize that various modifications and changes may bemade to the present invention without strictly following the exemplaryembodiments and applications illustrated and described herein. Forexample, various thresholds as discussed herein may be used in aninverse sense, with embodiments described as involving a determinationof a value exceeding the threshold can be implemented via values thatfall below a threshold. In addition, the various manners in which todenoise signals, filter signals, combine or otherwise process signals asdiscussed in connection with certain embodiments, may also beimplemented with other embodiments. Similarly, aspects discussed inconnection with and/or shown in the figures may be implemented withother embodiments in other figures or otherwise discussed herein. Suchmodifications do not depart from the true spirit and scope of thepresent invention, including that set forth in the following claims.

What is claimed is:
 1. An apparatus comprising: a circuit-based computerconfigured and arranged with executable instructions to identify aT-wave offset point within a first time window of an ECG signal, andprovide the identified T-wave offset point as an output based upon anoise characteristic of the ECG signal in a second time window thatincludes at least a portion of the T-wave of the ECG signal.
 2. Theapparatus of claim 1, wherein the circuit-based computer is configuredand arranged with the executable instructions to identify the T-waveoffset point by identifying a location of a QRS complex in the ECGsignal, identifying the first time window based upon a location of theQRS complex, computing a T-wave emphasis signal, and evaluating theemphasis signal within the first time window to identify the T-waveoffset point as a first baseline point after a last significant peak orvalley of the emphasis signal.
 3. The apparatus of claim 2, wherein thecircuit-based computer is configured and arranged with the executableinstructions to compute the T-wave emphasis signal by: decomposing theECG signal into subcomponents, identifying subcomponents in the firsttime window as either primarily associated with noise or primarilyassociated with the T-wave of the underlying ECG signal, and using atleast one of the subcomponents primarily associated with the T-wave asthe emphasis signal.
 4. The apparatus of claim 1, further including anon-transitory electronic data storage medium that includes theinstructions stored thereupon.
 5. The apparatus of claim 1, wherein thesecond time window includes the identified T-wave offset point.
 6. Theapparatus of claim 1, wherein the second time window has a duration ofabout 30% of the duration of a nominal QT interval of the ECG signal,and extends beyond the T-wave offset point by about 10% of the nominalQT interval duration.
 7. The apparatus of claim 1, wherein the secondtime window has a duration of about 50 msec and is centered on about theidentified T-wave offset point.
 8. The apparatus of claim 1, wherein thesecond time window extends from about the point of a largest deflectionof the T-wave from an isoelectric line of the ECG signal to about theT-wave offset point.
 9. The apparatus of claim 1, wherein the secondtime window includes a time period extending from about a QRS offsetpoint in the ECG signal to about the T-wave offset point.
 10. Theapparatus of claim 1, wherein the second time window includes a timeperiod extending the full duration of a cardiac cycle in the ECG signal.11. The apparatus of claim 1, wherein the circuit-based computer isconfigured and arranged with the executable instructions to compute thenoise characteristic by: decomposing the ECG signal into subcomponents,identifying said subcomponents in the second time window as eitherprimarily associated with noise or primarily associated with the T-waveof the underlying ECG signal, computing a noise signal for the secondtime window by using at least one subcomponent not primarily associatedwith the T-wave, and computing said noise characteristic based upon atleast one of: energy of the noise signal, standard deviation of thenoise signal, zero crossing density of the noise signal, a metric ofnoise amplitude computed using an envelope of the noise signal, and ametric of energy of the ECG signal relative to energy of the noisesignal.
 12. The apparatus of claim 11, wherein the circuit-basedcomputer is configured and arranged with the executable instructions tocompute a metric of energy of the ECG signal using at least one of thesubcomponents within the second time window that are primarilyassociated with the T-wave.
 13. The apparatus of claim 1, wherein thecircuit-based computer is configured and arranged with the executableinstructions to compute the noise characteristic by: computing adenoised signal for the second time window using at least one of aband-pass filter, wavelet thresholding, and an adaptive filter thatpasses primarily T-wave energy, capturing a residual of the step ofcomputing the denoised signal as a noise signal for the second timewindow, and computing said noise characteristic based upon at least oneof: energy of the noise signal, standard deviation of the noise signal,zero crossing density of the noise signal, a metric of noise amplitudecomputed using an envelope of the noise signal, and a metric of energyof the denoised signal relative to energy of the noise signal.
 14. Theapparatus of claim 1, wherein the circuit-based computer is configuredand arranged with the executable instructions to provide the identifiedT-wave offset point as an output based upon a noise characteristic ofthe ECG signal in the second time window by comparing the noisecharacteristic to a threshold and providing the identified T-wave offsetpoint based upon the comparison.
 15. The apparatus of claim 1, whereinthe circuit-based computer is configured and arranged with theexecutable instructions to provide the identified T-wave offset pointbased upon a presence, in a cardiac cycle within the ECG, of at leastone of: atrial fibrillation, QT dispersion in a multi-lead ECG exceedinga threshold, T-wave morphology complexity exceeding a predefinedthreshold, ventricular ectopy, a QT interval measurement that fallsoutside of a user-defined physiologic outlier value or a statisticaloutlier value.
 16. The apparatus of claim 1, wherein the circuit-basedcomputer is configured and arranged with the executable instructions toassemble a time series of provided QT interval values for analysis ofvariability using the provided identified T-wave offset point.
 17. Anapparatus for providing a repolarization signal for a cardiac cycle ofan ECG signal, the apparatus comprising: a circuit-based computerconfigured and arranged with executable instructions to identify thelocation of a QRS complex in the cardiac cycle; identify T-wave onsetand offset points based on the identified location of the QRS complex;define the start and end of the repolarization signal for the cardiaccycle, based respectively upon the T-wave onset and offset points;determine a noise characteristic of the ECG signal in a time windowspanning from about the start to about the end of the repolarizationsignal, and providing the repolarization signal as an output, based uponthe determined noise characteristic.
 18. The apparatus of claim 17,wherein the circuit-based computer is configured and arranged with theexecutable instructions to determine the noise characteristic by:decomposing the ECG signal into subcomponents, identifying ones of saidsubcomponents of the ECG signal within the time window as primarilyassociated with the T-wave of the underlying ECG signal, computing anoise signal for the time window using at least one of the subcomponentsnot primarily associated with the T-wave, and computing said noisecharacteristic based upon at least one of: energy of the noise signal,standard deviation of the noise signal, zero crossing density of thenoise signal, a metric of noise amplitude computed using an envelope ofthe noise signal, and a metric of energy of the ECG signal relative toenergy of the noise signal.
 19. The apparatus of claim 18, wherein thecircuit-based computer is configured and arranged with the executableinstructions to compute a metric of energy of the ECG signal using atleast one of the subcomponents within the second time window that isprimarily associated with the T-wave.
 20. The apparatus of claim 17,wherein the circuit-based computer is configured and arranged with theexecutable instructions to compute an output repolarization signal bydecomposing the ECG signal into subcomponents, identifying ones of saidsubcomponents of the ECG signal within the time window as primarilyassociated with the T-wave of the underlying ECG signal, and computingsaid output repolarization signal by combining at least two of thesubcomponents that are primarily associated with the T-wave.
 21. Theapparatus of claim 17, wherein the circuit-based computer is configuredand arranged with the executable instructions to compute the noisecharacteristic by: computing a denoised signal for the time window usingat least one of a band-pass filter, wavelet thresholding, and anadaptive filter that passes primarily T-wave energy, computing a noisesignal for the time window using a residual of the step of computing thedenoised signal, and computing said noise characteristic based upon atleast one of: energy of the noise signal, standard deviation of thenoise signal, zero crossing density of the noise signal, a metric ofnoise amplitude computed using an envelope of the noise signal, and ametric of energy of the denoised signal relative to energy of the noisesignal.
 22. The apparatus of claim 17, wherein the circuit-basedcomputer is configured and arranged with the executable instructions tocompare the noise characteristic to a threshold and provide therepolarization signal as an output based upon the comparison.
 23. Theapparatus of claim 17, wherein the circuit-based computer is configuredand arranged with the executable instructions to provide arepolarization signal as an output based upon the determined noisecharacteristic and the presence in the cardiac cycle of at least one of:atrial fibrillation in the ECG signal, a degree of QT dispersionexceeding a threshold when the ECG signal is a multi-lead signal, T-wavemorphology complexity of the ECG signal exceeding a threshold,ventricular ectopy, and a QT interval measurement that falls outside ofa user-defined physiologic outlier value or a statistical outlier value.24. The apparatus of claim 17, wherein the circuit-based computer isconfigured and arranged with the executable instructions to append theprovided repolarization signal to a matrix of repolarization signals inwhich a dimension of the matrix corresponds to the number of cardiaccycles of the ECG signal.
 25. An apparatus for providing a time seriesof beat-to-beat QT interval values from a digitized ECG signal of anambulatory subject, the apparatus comprising: a circuit-based computerconfigured and arranged with executable instructions to identify thelocation of a QRS complex and a Q-wave onset point of a cardiac cycle ofthe ECG signal; determine a first time window of the cardiac cycle inwhich to search for a T-wave offset point for a T-wave in the cardiaccycle, based upon one of the identified location of the QRS complex andthe identified location of the Q-wave onset point; identify the T-waveoffset point within the first time window; compute a QT interval valueusing the identified Q-wave onset point of the QRS complex and theidentified T-wave offset point, and provide the QT interval value in atime series of beat-to-beat QT interval values, based upon a noisecharacteristic of the digitized ECG signal in a second time window thatincludes at least a portion of the T-wave.
 26. The apparatus of claim25, wherein the circuit-based computer is configured and arranged withthe executable instructions to, prior to identifying the T-wave offsetpoint, suppress energy in a portion of the digitized ECG signal that isnot primarily associated with T-wave energy in the first time window,using at least one of MDSP denoising, wavelet threshold denoising,band-pass filtering, and adaptive filtering.
 27. The apparatus of claim25, wherein the circuit-based computer is configured and arranged withthe executable instructions to compute the noise characteristic by:decomposing the digitized ECG signal into subcomponents, identifyingsaid subcomponents as primarily associated with either noise or a T-waveof an underlying ECG signal, computing a noise signal by combining thesubcomponents within the second time window that are primarilyassociated with noise, computing a denoised signal by combining thesubcomponents within the second time window that are primarilyassociated with the T-wave of the underlying ECG signal, and computingsaid noise characteristic based upon at least one of: energy of thenoise signal, standard deviation of the noise signal, zero crossingdensity of the noise signal, a metric of noise amplitude based upon anenvelope of the noise signal, and a metric of energy of the denoisedsignal relative to energy of the noise signal.
 28. The apparatus ofclaim 27, wherein the metric of energy of the denoised signal relativeto energy of the noise signal is a signal-to-noise ratio.
 29. Theapparatus of claim 25, wherein the circuit-based computer is configuredand arranged with the executable instructions to compute the noisecharacteristic by: computing a denoised signal for the second timewindow using at least one of a band-pass filter, wavelet thresholding,and an adaptive filter, and computing a noise signal as a differencebetween the digitized ECG signal and the denoised signal, and computingsaid noise characteristic based upon at least one of: energy of thenoise signal, standard deviation of the noise signal, zero crossingdensity of the noise signal, a metric of noise amplitude computed usingan envelope of the noise signal, and a metric of energy of the denoisedsignal relative to energy of the noise signal.
 30. The apparatus ofclaim 25, wherein the circuit-based computer is configured and arrangedwith the executable instructions to provide the QT interval value in thetime series of beat-to-beat QT interval values based upon a comparisonof the noise characteristic to a threshold value.
 31. The apparatus ofclaim 25, wherein the circuit-based computer is configured and arrangedwith the executable instructions to provide the QT interval value in thetime series of beat-to-beat QT interval values based upon the noisecharacteristic and the presence, in the cardiac cycle, of at least oneof: atrial fibrillation, ventricular ectopy, QT dispersion in amulti-lead ECG exceeding a threshold, T-wave morphology complexityexceeding a predefined threshold, ventricular ectopy, a QT intervalmeasurement that falls outside of a user-defined physiologic outliervalue or a statistical outlier value.
 32. The apparatus of claim 25,wherein the circuit-based computer is configured and arranged with theexecutable instructions to repeat the steps to compute a plurality ofthe QT interval values, and provide ones of the QT interval values inthe time series of beat-to-beat QT interval values based upon acomparison of a noise characteristic of a corresponding ECG signal inthe second window to a predefined threshold.
 33. The apparatus of claim25, wherein the circuit-based computer is configured and arranged withthe executable instructions to compute a marker of arrhythmogenic riskby computing, using the beat-to-beat QT interval values, at least oneof: QT interval alternans, short-term variability of QT intervals,long-term variability of QT intervals, root mean square, standarddeviation of QT interval successive differences, and multiscale entropy.34. The apparatus of claim 25, wherein the circuit-based computer isconfigured and arranged with the executable instructions to provide thetime series of beat-to-beat QT intervals consisting of QT intervalvalues having error due to noise that is less than 2.5% of a mean QTinterval of the ECG signal.
 35. A computer program product, comprising:a non-transitory computer readable medium storing executable programinstructions, which when executed by a computing apparatus, cause thecomputing apparatus system to perform a method comprising: identifying aT-wave offset point within a first time window of an ECG signal, andproviding the identified T-wave offset point as an output based upon anoise characteristic of the ECG signal in a second time window thatincludes at least a portion of the T-wave of the ECG signal.
 36. Thecomputer program product of claim 35, wherein the non-transitorycomputer readable medium stores executable program instructions that,which when executed by the computing apparatus, causes the computingapparatus system to identify the location of a QRS complex in the ECGsignal, and determine the first time window based upon the identifiedlocation of the QRS complex.